Information Entropy and Interaction Optimization Model Based on Swarm Intelligence
By introducing the information entropy H(X) and mutual information I(X;Y) of information theory into swarm intelligence, the Interaction Optimization Model (IOM) is proposed. In this model, the information interaction process of individuals is analyzed with H(X) and I(X;Y) aiming at solving optimization problems. We call this optimization approach as interaction optimization. In order to validate this model, we proposed a new algorithm for Traveling Salesman Problem (TSP), namely Route-Exchange Algorithm (REA), which is inspired by the information interaction of individuals in swarm intelligence. Some benchmarks are tested in the experiments. The results indicate that the algorithm can quickly converge to the optimal solution with quite low cost.
KeywordsParticle Swarm Optimizer Mutual Information Travel Salesman Problem Travel Salesman Problem Information Entropy
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- 1.Bonabeau, E., Dorigo, M., Theraulaz, G.: Swarm Intelligence-from Natural to Artificial System. Oxford University Press, New York (1999)Google Scholar
- 2.Kennedy, J., Eberhart, R.C.: Particle Swarm Optimization. In: Proceedings of IEEE International Conference on Neural Networks, Piscataway, NJ, pp. 1942–1948 (1995)Google Scholar
- 3.Grefenstette, J., Gopal, R., Rosimaita, B., Van Gucht, D.: Genetic algorithms for the traveling salesman problem. In: Proceedings of the International Conference on Genetics Algorithms and their Applications, pp. 160–168 (1985)Google Scholar
- 4.Yao, X.: Evolutionary Computation: Theory and Applications. World Scientific, Singapore (1999)Google Scholar
- 6.Liu, J., Zhong, W.C., Liu, F., Jiao, L.C.: Organizational coevolutionary classification algorithm for radar target recognition. Journal of Infrared and Millimeter Waves 23(3), 208–212 (2004)Google Scholar
- 7.Han, J., Cai, Q.S.: Emergent Intelligence in AER Model. Chinese Journal of Pattern Recognition and Artificial Intelligence 15(2), 134–142 (2002)Google Scholar
- 9.Kennedy, J., Eberhart, R.C., Shi, Y.: Swarm Intelligence. Morgan Kaufmann Publishers, San Francisco (2001)Google Scholar